Investigating the dissemination of STEM content on social media with computational tools

arXiv:2404.18944v1 Announce Type: cross
Abstract: Social media platforms can quickly disseminate STEM content to diverse audiences, but their operation can be mysterious. We used open-source machine learning methods such as clustering, regression, and sentiment analysis to analyze over 1000 videos and metrics thereof from 6 social media STEM creators. Our data provide insights into how audiences generate interest signals(likes, bookmarks, comments, shares), on the correlation of various signals with views, and suggest that content from newer creators is disseminated differently. …

abstract analysis analyze arxiv clustering computational cs.cy cs.lg cs.si diverse machine machine learning media metrics platforms regression sentiment sentiment analysis social social media social media platforms stem tools type videos